Existing search engines often return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and the corresponding sample quotation to identify the results. This can be time consuming when multiple subtopics of the given query are mixed together.
Several previous solutions suggest clustering search results into different groups. However such traditional clustering techniques are inadequate since they do not generate clusters with highly readable names. Another previous solution utilizes a regression model learned from human label training data. Such data would first have to be built and accumulated to be accurate.